Overview

Dataset statistics

Number of variables15
Number of observations70
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.3 KiB
Average record size in memory121.9 B

Variable types

Text1
Numeric13
Categorical1

Alerts

APF is highly overall correlated with Hf(eV) and 4 other fieldsHigh correlation
EC(eV) is highly overall correlated with EV(eV)High correlation
EV(eV) is highly overall correlated with EC(eV)High correlation
Eg(PBE; eV) is highly overall correlated with cHigh correlation
Hf(eV) is highly overall correlated with APF and 3 other fieldsHigh correlation
V(angstrom^3-atom) is highly overall correlated with APF and 3 other fieldsHigh correlation
a(angstrom) is highly overall correlated with c and 2 other fieldsHigh correlation
c is highly overall correlated with APF and 2 other fieldsHigh correlation
d(angstrom) is highly overall correlated with a(angstrom) and 1 other fieldsHigh correlation
delta(d)(angstrom) is highly overall correlated with a(angstrom) and 1 other fieldsHigh correlation
phi(L)(eV) is highly overall correlated with phi(N)(eV)High correlation
phi(N)(eV) is highly overall correlated with APF and 4 other fieldsHigh correlation
phi(U)(eV) is highly overall correlated with APF and 3 other fieldsHigh correlation
c is highly imbalanced (74.5%)Imbalance

Reproduction

Analysis started2023-12-05 12:33:01.657720
Analysis finished2023-12-05 12:33:13.607716
Duration11.95 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

MXene
Text

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size692.0 B
2023-12-05T20:33:13.718931image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length15
Median length14
Mean length12.414286
Min length9

Characters and Unicode

Total characters869
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)97.1%

Sample

1st rowY-Sc-C-Cl-OCN
2nd rowY-Sc-C-Br-OCN
3rd rowY-Sc-C-NCO-Cl
4th rowY-Sc-C-NCS-H
5th rowY-Sc-C-NCS-Cl
ValueCountFrequency (%)
sc-y-c-cl-oh 2
 
2.9%
sc-sc-c-cn-ncs 1
 
1.4%
y-sc-c-nco-cl 1
 
1.4%
y-sc-c-ncs-h 1
 
1.4%
y-sc-c-ncs-cl 1
 
1.4%
y-sc-c-ncs-br 1
 
1.4%
y-sc-c-nco-oh 1
 
1.4%
sc-y-c-cn-f 1
 
1.4%
y-sc-c-ncs-oh 1
 
1.4%
y-sc-c-br-ocn 1
 
1.4%
Other values (59) 59
84.3%
2023-12-05T20:33:13.977539image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 280
32.2%
C 150
17.3%
S 97
 
11.2%
c 85
 
9.8%
N 57
 
6.6%
Y 55
 
6.3%
O 41
 
4.7%
H 27
 
3.1%
l 23
 
2.6%
B 23
 
2.6%
Other values (2) 31
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 458
52.7%
Dash Punctuation 280
32.2%
Lowercase Letter 131
 
15.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 150
32.8%
S 97
21.2%
N 57
 
12.4%
Y 55
 
12.0%
O 41
 
9.0%
H 27
 
5.9%
B 23
 
5.0%
F 8
 
1.7%
Lowercase Letter
ValueCountFrequency (%)
c 85
64.9%
l 23
 
17.6%
r 23
 
17.6%
Dash Punctuation
ValueCountFrequency (%)
- 280
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 589
67.8%
Common 280
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
C 150
25.5%
S 97
16.5%
c 85
14.4%
N 57
 
9.7%
Y 55
 
9.3%
O 41
 
7.0%
H 27
 
4.6%
l 23
 
3.9%
B 23
 
3.9%
r 23
 
3.9%
Common
ValueCountFrequency (%)
- 280
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 869
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 280
32.2%
C 150
17.3%
S 97
 
11.2%
c 85
 
9.8%
N 57
 
6.6%
Y 55
 
6.3%
O 41
 
4.7%
H 27
 
3.1%
l 23
 
2.6%
B 23
 
2.6%
Other values (2) 31
 
3.6%

Eg(GW; eV)
Real number (ℝ)

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4334343
Minimum1.5867
Maximum3.3956
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:14.086599image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.5867
5-th percentile1.842585
Q12.28305
median2.40125
Q32.571175
95-th percentile3.26124
Maximum3.3956
Range1.8089
Interquartile range (IQR)0.288125

Descriptive statistics

Standard deviation0.37745806
Coefficient of variation (CV)0.15511332
Kurtosis1.0760585
Mean2.4334343
Median Absolute Deviation (MAD)0.15005
Skewness0.497957
Sum170.3404
Variance0.14247459
MonotonicityNot monotonic
2023-12-05T20:33:14.244667image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.392 2
 
2.9%
3.3956 1
 
1.4%
2.3367 1
 
1.4%
2.2844 1
 
1.4%
2.2863 1
 
1.4%
2.2938 1
 
1.4%
2.2946 1
 
1.4%
2.3178 1
 
1.4%
2.3317 1
 
1.4%
2.3368 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
1.5867 1
1.4%
1.6075 1
1.4%
1.7317 1
1.4%
1.8312 1
1.4%
1.8565 1
1.4%
1.8966 1
1.4%
1.9271 1
1.4%
1.9891 1
1.4%
2.0114 1
1.4%
2.0275 1
1.4%
ValueCountFrequency (%)
3.3956 1
1.4%
3.3659 1
1.4%
3.3072 1
1.4%
3.2661 1
1.4%
3.2553 1
1.4%
3.2203 1
1.4%
2.9966 1
1.4%
2.7883 1
1.4%
2.787 1
1.4%
2.6927 1
1.4%

Eg(PBE; eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.93646429
Minimum0.7024
Maximum1.3781
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:14.359627image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.7024
5-th percentile0.7465
Q10.844375
median0.9251
Q31.02625
95-th percentile1.15781
Maximum1.3781
Range0.6757
Interquartile range (IQR)0.181875

Descriptive statistics

Standard deviation0.13208056
Coefficient of variation (CV)0.14104175
Kurtosis0.85128463
Mean0.93646429
Median Absolute Deviation (MAD)0.0944
Skewness0.66718998
Sum65.5525
Variance0.017445275
MonotonicityNot monotonic
2023-12-05T20:33:14.482871image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0334 2
 
2.9%
0.9262 2
 
2.9%
0.8981 2
 
2.9%
1.1194 1
 
1.4%
0.8938 1
 
1.4%
1.057 1
 
1.4%
1.017 1
 
1.4%
0.79 1
 
1.4%
0.7528 1
 
1.4%
0.8756 1
 
1.4%
Other values (57) 57
81.4%
ValueCountFrequency (%)
0.7024 1
1.4%
0.7184 1
1.4%
0.7363 1
1.4%
0.742 1
1.4%
0.752 1
1.4%
0.7523 1
1.4%
0.7528 1
1.4%
0.768 1
1.4%
0.7887 1
1.4%
0.79 1
1.4%
ValueCountFrequency (%)
1.3781 1
1.4%
1.2592 1
1.4%
1.1849 1
1.4%
1.1741 1
1.4%
1.1379 1
1.4%
1.1194 1
1.4%
1.1042 1
1.4%
1.0857 1
1.4%
1.0744 1
1.4%
1.057 1
1.4%

phi(L)(eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0187471
Minimum-1.0978
Maximum4.6033
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)5.7%
Memory size692.0 B
2023-12-05T20:33:14.597822image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-1.0978
5-th percentile0.086975
Q11.401725
median2.01525
Q33.0736
95-th percentile3.66273
Maximum4.6033
Range5.7011
Interquartile range (IQR)1.671875

Descriptive statistics

Standard deviation1.2150407
Coefficient of variation (CV)0.60187861
Kurtosis-0.29881322
Mean2.0187471
Median Absolute Deviation (MAD)0.9627
Skewness-0.33914182
Sum141.3123
Variance1.4763239
MonotonicityNot monotonic
2023-12-05T20:33:14.701081image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4122 2
 
2.9%
1.8022 1
 
1.4%
3.2009 1
 
1.4%
3.6393 1
 
1.4%
1.9165 1
 
1.4%
3.6819 1
 
1.4%
3.1326 1
 
1.4%
3.1819 1
 
1.4%
2.1713 1
 
1.4%
1.5756 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
-1.0978 1
1.4%
-0.7587 1
1.4%
-0.2694 1
1.4%
-0.0658 1
1.4%
0.2737 1
1.4%
0.4122 2
2.9%
0.4702 1
1.4%
0.4785 1
1.4%
0.5124 1
1.4%
0.5764 1
1.4%
ValueCountFrequency (%)
4.6033 1
1.4%
4.2489 1
1.4%
3.8917 1
1.4%
3.6819 1
1.4%
3.6393 1
1.4%
3.4288 1
1.4%
3.4256 1
1.4%
3.3325 1
1.4%
3.3014 1
1.4%
3.2331 1
1.4%

phi(U)(eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.59937
Minimum1.5546
Maximum8.3334
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:14.800663image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.5546
5-th percentile2.77035
Q13.277975
median4.57295
Q35.66635
95-th percentile6.90117
Maximum8.3334
Range6.7788
Interquartile range (IQR)2.388375

Descriptive statistics

Standard deviation1.4194353
Coefficient of variation (CV)0.30861515
Kurtosis-0.30922247
Mean4.59937
Median Absolute Deviation (MAD)1.28175
Skewness0.30655178
Sum321.9559
Variance2.0147965
MonotonicityNot monotonic
2023-12-05T20:33:14.907142image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.7798 2
 
2.9%
2.6655 1
 
1.4%
3.2717 1
 
1.4%
5.0062 1
 
1.4%
3.2721 1
 
1.4%
5.0224 1
 
1.4%
5.8906 1
 
1.4%
5.8591 1
 
1.4%
6.3336 1
 
1.4%
7.1547 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
1.5546 1
1.4%
1.8893 1
1.4%
2.6655 1
1.4%
2.7267 1
1.4%
2.8237 1
1.4%
2.8531 1
1.4%
2.9504 1
1.4%
2.9925 1
1.4%
3.114 1
1.4%
3.1195 1
1.4%
ValueCountFrequency (%)
8.3334 1
1.4%
7.5205 1
1.4%
7.5012 1
1.4%
7.1547 1
1.4%
6.5913 1
1.4%
6.4349 1
1.4%
6.3941 1
1.4%
6.3336 1
1.4%
6.3305 1
1.4%
6.3285 1
1.4%

d(angstrom)
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4062671
Minimum2.2806
Maximum2.5706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:15.104880image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum2.2806
5-th percentile2.307295
Q12.3412
median2.4223
Q32.440075
95-th percentile2.535525
Maximum2.5706
Range0.29
Interquartile range (IQR)0.098875

Descriptive statistics

Standard deviation0.066480931
Coefficient of variation (CV)0.027628242
Kurtosis-0.1746171
Mean2.4062671
Median Absolute Deviation (MAD)0.0296
Skewness0.14302152
Sum168.4387
Variance0.0044197141
MonotonicityNot monotonic
2023-12-05T20:33:15.207828image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.4285 2
 
2.9%
2.4407 2
 
2.9%
2.4223 2
 
2.9%
2.4302 1
 
1.4%
2.4553 1
 
1.4%
2.4244 1
 
1.4%
2.4316 1
 
1.4%
2.4225 1
 
1.4%
2.4253 1
 
1.4%
2.424 1
 
1.4%
Other values (57) 57
81.4%
ValueCountFrequency (%)
2.2806 1
1.4%
2.2973 1
1.4%
2.3052 1
1.4%
2.3059 1
1.4%
2.309 1
1.4%
2.3097 1
1.4%
2.3101 1
1.4%
2.3117 1
1.4%
2.3164 1
1.4%
2.3189 1
1.4%
ValueCountFrequency (%)
2.5706 1
1.4%
2.5568 1
1.4%
2.5379 1
1.4%
2.5362 1
1.4%
2.5347 1
1.4%
2.507 1
1.4%
2.4873 1
1.4%
2.4671 1
1.4%
2.4558 1
1.4%
2.4553 1
1.4%

delta(d)(angstrom)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.03293
Minimum-0.1108
Maximum0.2643
Zeros0
Zeros (%)0.0%
Negative48
Negative (%)68.6%
Memory size692.0 B
2023-12-05T20:33:15.309974image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-0.1108
5-th percentile-0.07746
Q1-0.055275
median-0.04155
Q30.203625
95-th percentile0.237245
Maximum0.2643
Range0.3751
Interquartile range (IQR)0.2589

Descriptive statistics

Standard deviation0.12948281
Coefficient of variation (CV)3.9320622
Kurtosis-1.295687
Mean0.03293
Median Absolute Deviation (MAD)0.02375
Skewness0.78973427
Sum2.3051
Variance0.016765798
MonotonicityNot monotonic
2023-12-05T20:33:15.418696image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.0434 2
 
2.9%
0.2351 2
 
2.9%
-0.0617 2
 
2.9%
-0.0763 2
 
2.9%
0.2174 1
 
1.4%
-0.0526 1
 
1.4%
-0.0435 1
 
1.4%
-0.0463 1
 
1.4%
-0.045 1
 
1.4%
-0.0512 1
 
1.4%
Other values (56) 56
80.0%
ValueCountFrequency (%)
-0.1108 1
1.4%
-0.097 1
1.4%
-0.0881 1
1.4%
-0.078 1
1.4%
-0.0768 1
1.4%
-0.0763 2
2.9%
-0.0748 1
1.4%
-0.0733 1
1.4%
-0.0725 1
1.4%
-0.0658 1
1.4%
ValueCountFrequency (%)
0.2643 1
1.4%
0.2476 1
1.4%
0.2396 1
1.4%
0.239 1
1.4%
0.2351 2
2.9%
0.2348 1
1.4%
0.2331 1
1.4%
0.2285 1
1.4%
0.2259 1
1.4%
0.2254 1
1.4%

V(angstrom^3-atom)
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.829944
Minimum29.6856
Maximum69.3866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:15.527861image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum29.6856
5-th percentile30.28584
Q138.41925
median44.84955
Q356.230075
95-th percentile64.566965
Maximum69.3866
Range39.701
Interquartile range (IQR)17.810825

Descriptive statistics

Standard deviation10.971596
Coefficient of variation (CV)0.23428591
Kurtosis-1.1065051
Mean46.829944
Median Absolute Deviation (MAD)9.54735
Skewness0.20526569
Sum3278.0961
Variance120.37592
MonotonicityNot monotonic
2023-12-05T20:33:15.635664image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60.3739 9
 
12.9%
50.3116 5
 
7.1%
43.1242 4
 
5.7%
33.5411 3
 
4.3%
37.7337 2
 
2.9%
36.5156 1
 
1.4%
30.3151 1
 
1.4%
55.492 1
 
1.4%
56.459 1
 
1.4%
45.009 1
 
1.4%
Other values (42) 42
60.0%
ValueCountFrequency (%)
29.6856 1
 
1.4%
30.0594 1
 
1.4%
30.0753 1
 
1.4%
30.2619 1
 
1.4%
30.3151 1
 
1.4%
31.9512 1
 
1.4%
32.8645 1
 
1.4%
33.4194 1
 
1.4%
33.5411 3
4.3%
33.9854 1
 
1.4%
ValueCountFrequency (%)
69.3866 1
 
1.4%
67.0229 1
 
1.4%
65.7179 1
 
1.4%
64.6445 1
 
1.4%
64.4722 1
 
1.4%
60.4362 1
 
1.4%
60.3739 9
12.9%
56.8957 1
 
1.4%
56.789 1
 
1.4%
56.459 1
 
1.4%

APF
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.33379429
Minimum0.2222
Maximum0.5371
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:15.737721image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0.2222
5-th percentile0.226145
Q10.269525
median0.3139
Q30.3764
95-th percentile0.489895
Maximum0.5371
Range0.3149
Interquartile range (IQR)0.106875

Descriptive statistics

Standard deviation0.080266958
Coefficient of variation (CV)0.24046834
Kurtosis-0.45617236
Mean0.33379429
Median Absolute Deviation (MAD)0.05565
Skewness0.63066136
Sum23.3656
Variance0.0064427846
MonotonicityNot monotonic
2023-12-05T20:33:15.840622image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3113 3
 
4.3%
0.3661 2
 
2.9%
0.2921 1
 
1.4%
0.2316 1
 
1.4%
0.4894 1
 
1.4%
0.3069 1
 
1.4%
0.2226 1
 
1.4%
0.2233 1
 
1.4%
0.3076 1
 
1.4%
0.2262 1
 
1.4%
Other values (57) 57
81.4%
ValueCountFrequency (%)
0.2222 1
1.4%
0.2226 1
1.4%
0.2233 1
1.4%
0.2261 1
1.4%
0.2262 1
1.4%
0.2264 1
1.4%
0.2316 1
1.4%
0.2543 1
1.4%
0.2546 1
1.4%
0.2551 1
1.4%
ValueCountFrequency (%)
0.5371 1
1.4%
0.4936 1
1.4%
0.4933 1
1.4%
0.4903 1
1.4%
0.4894 1
1.4%
0.4837 1
1.4%
0.464 1
1.4%
0.4358 1
1.4%
0.4286 1
1.4%
0.4278 1
1.4%

EV(eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-5.1034443
Minimum-9.7649
Maximum-2.0598
Zeros0
Zeros (%)0.0%
Negative70
Negative (%)100.0%
Memory size692.0 B
2023-12-05T20:33:15.944818image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-9.7649
5-th percentile-8.242595
Q1-5.890425
median-5.2562
Q3-3.8024
95-th percentile-2.672925
Maximum-2.0598
Range7.7051
Interquartile range (IQR)2.088025

Descriptive statistics

Standard deviation1.7533843
Coefficient of variation (CV)-0.34356882
Kurtosis-0.14263031
Mean-5.1034443
Median Absolute Deviation (MAD)1.2751
Skewness-0.52675764
Sum-357.2411
Variance3.0743565
MonotonicityNot monotonic
2023-12-05T20:33:16.052433image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-2.7664 2
 
2.9%
-5.2978 1
 
1.4%
-3.9975 1
 
1.4%
-5.7805 1
 
1.4%
-3.9051 1
 
1.4%
-5.7934 1
 
1.4%
-3.6592 1
 
1.4%
-3.6134 1
 
1.4%
-5.6868 1
 
1.4%
-7.1885 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
-9.7649 1
1.4%
-9.2811 1
1.4%
-8.7583 1
1.4%
-8.4716 1
1.4%
-7.9627 1
1.4%
-7.7459 1
1.4%
-7.6261 1
1.4%
-7.4028 1
1.4%
-7.1885 1
1.4%
-7.1331 1
1.4%
ValueCountFrequency (%)
-2.0598 1
1.4%
-2.4807 1
1.4%
-2.5465 1
1.4%
-2.6538 1
1.4%
-2.6963 1
1.4%
-2.7664 2
2.9%
-2.8827 1
1.4%
-2.9442 1
1.4%
-3.1205 1
1.4%
-3.1444 1
1.4%

EC(eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-4.1669743
Minimum-9.0625
Maximum-1.2593
Zeros0
Zeros (%)0.0%
Negative70
Negative (%)100.0%
Memory size692.0 B
2023-12-05T20:33:16.159538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-9.0625
5-th percentile-7.28688
Q1-4.959825
median-4.2719
Q3-2.93915
95-th percentile-1.76467
Maximum-1.2593
Range7.8032
Interquartile range (IQR)2.020675

Descriptive statistics

Standard deviation1.7369178
Coefficient of variation (CV)-0.41682951
Kurtosis0.02700062
Mean-4.1669743
Median Absolute Deviation (MAD)1.1997
Skewness-0.60098841
Sum-291.6882
Variance3.0168836
MonotonicityNot monotonic
2023-12-05T20:33:16.268694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.8683 2
 
2.9%
-4.4069 1
 
1.4%
-3.1037 1
 
1.4%
-4.7235 1
 
1.4%
-2.9789 1
 
1.4%
-4.776 1
 
1.4%
-2.8692 1
 
1.4%
-2.8606 1
 
1.4%
-4.8112 1
 
1.4%
-6.1857 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
-9.0625 1
1.4%
-8.3538 1
1.4%
-7.6829 1
1.4%
-7.4991 1
1.4%
-7.0275 1
1.4%
-6.9417 1
1.4%
-6.7485 1
1.4%
-6.3604 1
1.4%
-6.2069 1
1.4%
-6.1857 1
1.4%
ValueCountFrequency (%)
-1.2593 1
1.4%
-1.5386 1
1.4%
-1.688 1
1.4%
-1.7149 1
1.4%
-1.8255 1
1.4%
-1.8683 2
2.9%
-1.9632 1
1.4%
-2.1762 1
1.4%
-2.3024 1
1.4%
-2.3552 1
1.4%

phi(N)(eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3094614
Minimum1.5544
Maximum4.9302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:16.376881image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.5544
5-th percentile2.016885
Q12.6247
median3.23395
Q34.135375
95-th percentile4.624385
Maximum4.9302
Range3.3758
Interquartile range (IQR)1.510675

Descriptive statistics

Standard deviation0.8435669
Coefficient of variation (CV)0.25489552
Kurtosis-0.93228442
Mean3.3094614
Median Absolute Deviation (MAD)0.63795
Skewness0.0079822227
Sum231.6623
Variance0.71160511
MonotonicityNot monotonic
2023-12-05T20:33:16.482451image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.596 2
 
2.9%
2.2346 1
 
1.4%
3.2363 1
 
1.4%
4.3228 1
 
1.4%
2.5943 1
 
1.4%
4.3521 1
 
1.4%
4.5116 1
 
1.4%
4.5205 1
 
1.4%
4.2524 1
 
1.4%
4.3651 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
1.5544 1
1.4%
1.6943 1
1.4%
1.8545 1
1.4%
1.9569 1
1.4%
2.0902 1
1.4%
2.1651 1
1.4%
2.192 1
1.4%
2.2346 1
1.4%
2.2957 1
1.4%
2.3935 1
1.4%
ValueCountFrequency (%)
4.9302 1
1.4%
4.6635 1
1.4%
4.6598 1
1.4%
4.6418 1
1.4%
4.6031 1
1.4%
4.5205 1
1.4%
4.5116 1
1.4%
4.3651 1
1.4%
4.3521 1
1.4%
4.3228 1
1.4%

a(angstrom)
Real number (ℝ)

HIGH CORRELATION 

Distinct68
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.5174529
Minimum3.2937
Maximum3.7748
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size692.0 B
2023-12-05T20:33:16.590107image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum3.2937
5-th percentile3.35687
Q13.447125
median3.53095
Q33.5754
95-th percentile3.715935
Maximum3.7748
Range0.4811
Interquartile range (IQR)0.128275

Descriptive statistics

Standard deviation0.10252224
Coefficient of variation (CV)0.029146729
Kurtosis-0.028508284
Mean3.5174529
Median Absolute Deviation (MAD)0.059
Skewness0.095637584
Sum246.2217
Variance0.010510811
MonotonicityNot monotonic
2023-12-05T20:33:16.698406image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.5318 2
 
2.9%
3.5099 2
 
2.9%
3.5492 1
 
1.4%
3.5367 1
 
1.4%
3.563 1
 
1.4%
3.5301 1
 
1.4%
3.5358 1
 
1.4%
3.535 1
 
1.4%
3.4191 1
 
1.4%
3.6109 1
 
1.4%
Other values (58) 58
82.9%
ValueCountFrequency (%)
3.2937 1
1.4%
3.3274 1
1.4%
3.3497 1
1.4%
3.3548 1
1.4%
3.3594 1
1.4%
3.3608 1
1.4%
3.3616 1
1.4%
3.3754 1
1.4%
3.3794 1
1.4%
3.385 1
1.4%
ValueCountFrequency (%)
3.7748 1
1.4%
3.7323 1
1.4%
3.7238 1
1.4%
3.7185 1
1.4%
3.7128 1
1.4%
3.6542 1
1.4%
3.639 1
1.4%
3.6267 1
1.4%
3.6216 1
1.4%
3.6109 1
1.4%

c
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size692.0 B
1
67 
0
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters70
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 67
95.7%
0 3
 
4.3%

Length

2023-12-05T20:33:16.791962image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-05T20:33:16.867680image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
1 67
95.7%
0 3
 
4.3%

Most occurring characters

ValueCountFrequency (%)
1 67
95.7%
0 3
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 67
95.7%
0 3
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
Common 70
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 67
95.7%
0 3
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 67
95.7%
0 3
 
4.3%

Hf(eV)
Real number (ℝ)

HIGH CORRELATION 

Distinct69
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-10.691054
Minimum-17.5949
Maximum-6.3118
Zeros0
Zeros (%)0.0%
Negative70
Negative (%)100.0%
Memory size692.0 B
2023-12-05T20:33:16.952556image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum-17.5949
5-th percentile-15.80852
Q1-12.656275
median-10.6437
Q3-8.430825
95-th percentile-6.529105
Maximum-6.3118
Range11.2831
Interquartile range (IQR)4.22545

Descriptive statistics

Standard deviation2.9291031
Coefficient of variation (CV)-0.27397701
Kurtosis-0.65941736
Mean-10.691054
Median Absolute Deviation (MAD)2.0778
Skewness-0.35164199
Sum-748.3738
Variance8.579645
MonotonicityNot monotonic
2023-12-05T20:33:17.062084image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-11.0999 2
 
2.9%
-6.6218 1
 
1.4%
-6.9571 1
 
1.4%
-13.8827 1
 
1.4%
-6.4954 1
 
1.4%
-13.8442 1
 
1.4%
-15.5024 1
 
1.4%
-15.5058 1
 
1.4%
-11.2764 1
 
1.4%
-8.5272 1
 
1.4%
Other values (59) 59
84.3%
ValueCountFrequency (%)
-17.5949 1
1.4%
-16.6708 1
1.4%
-16.349 1
1.4%
-16.0562 1
1.4%
-15.5058 1
1.4%
-15.5024 1
1.4%
-15.4334 1
1.4%
-14.2904 1
1.4%
-14.2635 1
1.4%
-14.1302 1
1.4%
ValueCountFrequency (%)
-6.3118 1
1.4%
-6.414 1
1.4%
-6.4854 1
1.4%
-6.4954 1
1.4%
-6.5703 1
1.4%
-6.6218 1
1.4%
-6.9516 1
1.4%
-6.9571 1
1.4%
-7.0017 1
1.4%
-7.0143 1
1.4%

Interactions

2023-12-05T20:33:12.475055image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:01.796710image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.648140image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.507804image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.425602image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.240302image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.061418image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.934364image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.908764image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.817538image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.687322image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.570030image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.659720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.541916image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:01.862561image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.715319image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.689388image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.490401image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.305685image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.129952image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.002820image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.019009image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.888609image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.756527image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.685631image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.722566image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.606616image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:01.929054image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.780228image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.750935image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.554145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.370188image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.200689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.071160image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.105849image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.957452image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.823950image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.759507image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.784611image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.666926image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:01.990629image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.842728image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.806583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.611313image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.429375image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.261509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.134161image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.166706image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.020332image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.889803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.825274image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.844803image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.729285image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.055532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.906340image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.867529image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.670811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.488670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.325919image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.199297image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.230657image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.084100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.955901image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.894677image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.906522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.790135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.119192image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.968691image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.925520image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.730169image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.546651image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.390532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.262943image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.292555image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.147729image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.017921image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.957682image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.967028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.860707image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.188035image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.036074image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.987494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.794044image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.612314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.456720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.331534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.359579image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.218034image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.087410image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.025669image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.031437image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.927747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.258444image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.106699image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.057922image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.865738image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.678748image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.527747image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.400994image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.428057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.288147image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.159344image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.143057image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.098486image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.989647image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.322868image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.170576image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.119857image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.926917image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.739334image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.592999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.561094image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.488083image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.356509image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.225345image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.321104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.159583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:13.061630image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.390715image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.241145image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.185201image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.992684image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.804379image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.664282image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.632135image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.559102image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.425847image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.298117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.391467image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.225353image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:13.134843image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.458310image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.312634image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.247725image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.057608image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.872045image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.736591image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.705670image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.627653image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.495601image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.370785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.467176image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.293015image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:13.200694image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.525898image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.381314image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.312171image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.121884image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.942807image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.802676image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.779583image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.694532image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.561769image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.445577image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.536999image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.357702image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:13.259723image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:02.585492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:03.440719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:04.367007image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:05.180236image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.000668image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:06.868934image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:07.840785image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:08.754040image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:09.624727image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:10.507363image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:11.596104image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-12-05T20:33:12.414012image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-12-05T20:33:17.141052image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
APFEC(eV)EV(eV)Eg(GW; eV)Eg(PBE; eV)Hf(eV)V(angstrom^3-atom)a(angstrom)cd(angstrom)delta(d)(angstrom)phi(L)(eV)phi(N)(eV)phi(U)(eV)
APF1.000-0.078-0.0810.284-0.242-0.769-0.9360.0690.7410.121-0.1000.1410.7160.747
EC(eV)-0.0781.0000.995-0.272-0.1540.1790.1220.2800.0000.290-0.330-0.332-0.416-0.177
EV(eV)-0.0810.9951.000-0.269-0.2250.1810.1150.2760.0000.283-0.325-0.323-0.407-0.175
Eg(GW; eV)0.284-0.272-0.2691.000-0.041-0.115-0.411-0.0440.000-0.040-0.0040.2170.4550.285
Eg(PBE; eV)-0.242-0.154-0.225-0.0411.0000.2340.311-0.0050.505-0.0000.050-0.112-0.238-0.168
Hf(eV)-0.7690.1790.181-0.1150.2341.0000.7470.1550.0000.101-0.126-0.094-0.511-0.579
V(angstrom^3-atom)-0.9360.1220.115-0.4110.3110.7471.0000.0670.1500.019-0.017-0.089-0.677-0.731
a(angstrom)0.0690.2800.276-0.044-0.0050.1550.0671.0000.6860.993-0.9650.3070.2870.050
c0.7410.0000.0000.0000.5050.0000.1500.6861.000-0.3370.3180.211-0.089-0.138
d(angstrom)0.1210.2900.283-0.040-0.0000.1010.0190.993-0.3371.000-0.9630.2830.3040.092
delta(d)(angstrom)-0.100-0.330-0.325-0.0040.050-0.126-0.017-0.9650.318-0.9631.000-0.285-0.339-0.145
phi(L)(eV)0.141-0.332-0.3230.217-0.112-0.094-0.0890.3070.2110.283-0.2851.0000.564-0.143
phi(N)(eV)0.716-0.416-0.4070.455-0.238-0.511-0.6770.287-0.0890.304-0.3390.5641.0000.695
phi(U)(eV)0.747-0.177-0.1750.285-0.168-0.579-0.7310.050-0.1380.092-0.145-0.1430.6951.000

Missing values

2023-12-05T20:33:13.384163image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-05T20:33:13.548681image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MXeneEg(GW; eV)Eg(PBE; eV)phi(L)(eV)phi(U)(eV)d(angstrom)delta(d)(angstrom)V(angstrom^3-atom)APFEV(eV)EC(eV)phi(N)(eV)a(angstrom)cHf(eV)
0Y-Sc-C-Cl-OCN3.39560.87562.17136.33362.4302-0.051239.01520.3661-5.6868-4.81124.25243.54921-11.2764
1Y-Sc-C-Br-OCN3.36590.82692.13406.39412.4424-0.063439.71890.3667-5.4308-4.60394.26413.57911-10.6495
2Y-Sc-C-NCO-Cl3.30720.92493.33254.56812.4290-0.050039.01580.3661-4.0290-3.10413.95043.55091-12.6013
3Y-Sc-C-NCS-H3.26610.92532.94833.20842.4139-0.034938.22060.3786-3.2805-2.35523.07833.51551-9.9570
4Y-Sc-C-NCS-Cl3.25530.81813.00554.34192.4407-0.061739.62600.3724-3.1205-2.30243.67373.57851-10.5544
5Y-Sc-C-NCS-Br3.22030.76803.09634.26362.4515-0.072540.30970.3731-2.9442-2.17623.67993.60951-9.9877
6Y-Sc-C-NCO-OH2.99660.91390.65266.28342.4051-0.026132.86450.4286-3.5707-2.65683.46793.48471-6.5703
7Sc-Y-C-CN-F2.78831.17411.20695.36052.4149-0.035944.48920.3111-5.4273-4.25323.28373.51131-11.0755
8Y-Sc-C-NCS-OH2.78700.80051.67244.56362.4171-0.038133.41940.4358-2.0598-1.25933.11803.51201-13.9745
9Sc-Y-C-Cl-OCN2.69270.90122.17686.33052.4324-0.053439.21150.3642-5.6489-4.74774.25363.55801-11.3974
MXeneEg(GW; eV)Eg(PBE; eV)phi(L)(eV)phi(U)(eV)d(angstrom)delta(d)(angstrom)V(angstrom^3-atom)APFEV(eV)EC(eV)phi(N)(eV)a(angstrom)cHf(eV)
60Y-Y-C-Br-Br2.02750.89433.23313.23312.5568-0.097060.37390.2839-5.6758-4.78153.23313.77481-16.0562
61Y-Sc-C-Cl-OH2.01140.91950.51244.78672.4174-0.038444.69010.3135-2.8827-1.96322.64953.51771-10.9679
62Y-Sc-C-Br-OH1.98910.87080.57644.66792.4285-0.049645.46320.3143-2.6963-1.82552.62293.54911-10.3250
63Sc-Sc-C-CN-OH1.92711.0067-1.09785.89862.30900.235143.12420.3221-5.2973-4.29062.40033.35941-12.6746
64Y-Sc-C-H-OH1.89660.96580.47023.44352.3922-0.013243.02690.3189-2.6538-1.68801.95693.45401-10.5869
65Y-Sc-C-F-OH1.85650.94210.61793.71232.3915-0.012542.97170.3179-2.4807-1.53862.16513.45101-13.2859
66Sc-Y-C-Br-OH1.83120.83160.47854.64372.4349-0.055945.89950.3113-2.5465-1.71492.56123.56431-10.5032
67Sc-Y-C-Cl-OH1.73170.89810.41224.77982.4223-0.043454.01090.3113-2.7664-1.86832.59603.53181-11.0999
68Sc-Sc-C-Br-OH1.60750.7363-0.75874.14742.33150.213450.31160.2734-3.1444-2.40811.69433.42021-10.6379
69Sc-Sc-C-Cl-OH1.58670.8588-0.06584.24622.31640.228550.31160.2732-3.7941-2.93532.09023.37941-11.3372